Estimated prevalence of obstructive sleep apnea by occupation and industry in England: a descriptive study
Bibliographic record
Abstract
Study Objectives: Obstructive sleep apnea (OSA) can induce excessive sleepiness, causing work-related injuries and low productivity. Most individuals with OSA in the United Kingdom are undiagnosed, and thus, theoretically, workplace screening, might by identifying these individuals improve both their individual health and overall productivity. However, the prevalence of OSA in different workplaces is unclear. This study aimed to estimate the prevalence of OSA by industries and occupations in England. Methods: The Health Survey for England 2019 dataset was combined with Sleep Heart Health Study dataset. We applied multiple imputation for the combined dataset to estimate OSA in the English population aged 40-64. We estimated the pooled prevalence of OSA by both industry and occupation by separating samples by Standard Industry Classification and Standard Occupation Classification. Results: The overall OSA prevalence estimated by imputation for ages 40-64 was 17.8% (95% CI = 15.9% to 19.9%). Separating those samples into industrial/occupational groups, the estimated prevalence of OSA varied widely by industry/occupation. Descriptive analysis revealed that the estimated prevalence of OSA was relatively higher in the Accommodation and food, Public administration and defence; compulsory social security, Construction industries, and Protective service occupations, health and social care associate professionals, and skilled construction and building trades occupations. Conclusions: In England in 2019, Accommodation and food, Public administration and defence; compulsory social security, Construction industries, and Protective service occupations, health and social care associate professionals, and skilled construction and building trades occupations showed a relatively higher prevalence of OSA indicating that they may be target populations for workplace screening.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".